Digital adherence technologies to improve tuberculosis treatment outcomes in China: a cluster-randomised superiority trial

Summary Background Drug-sensitive tuberculosis treatment requires 6 months of therapy, so adherence problems are common. Digital adherence technologies might improve tuberculosis treatment outcomes. We aimed to evaluate the effect of a daily reminder medication monitor, monthly review of adherence data by the health-care provider, and differentiated care for patients with adherence issues, on tuberculosis treatment adherence and outcomes. Methods We did a cluster-randomised superiority trial across four prefectures in China. 24 counties or districts (clusters) were randomly assigned (1:1) to intervention or control groups. We enrolled patients aged 18 years or older with GeneXpert-positive, rifampicin-sensitive pulmonary tuberculosis, who were receiving daily fixed-dose combination treatment. Patients in the intervention group received a medication monitor for daily drug-dosing reminders, monthly review of adherence data by health-care provider, and management of poor adherence; and patients in the control group received routine care (silent-mode monitor-measured adherence). Only the independent endpoints review committee who assessed endpoint data for some participants were masked to study group assignment. Patients were followed up (with sputum solid culture) at 12 and 18 months. The primary outcome was a composite of death, loss to follow-up, treatment failure, switch to multidrug-resistant tuberculosis treatment, or tuberculosis recurrence by 18 months from treatment start, analysed in the intention-to-treat population. Analysis accounted for study design with multiple imputation for the primary outcome. This trial is now complete and is registered with ISRCTN, 35812455. Findings Between Jan 26, 2017, and April 3, 2019, 15 257 patients were assessed for eligibility and 3074 were enrolled, 2686 (87%) of whom were included in the intention-to-treat population. 1909 (71%) of 2686 patients were male, 777 (29%) were female, and the median age was 44 years (IQR 29–58). By 18 months from treatment start, using multiple imputation for missing outcomes, 239 (16% [geometric mean of cluster-level proportion]) of 1388 patients in the control group and 224 (16%) of 1298 in the intervention group had a primary composite outcome event (289 [62%] of 463 events were loss to follow-up during treatment and 42 [9%] were tuberculosis recurrence). The intervention had no effect on risk of the primary composite outcome (adjusted risk ratio 1·01, 95% CI 0·73–1·40). Interpretation Our digital medication monitor intervention had no effect on unfavourable outcomes, which included loss to follow-up during treatment, tuberculosis recurrence, death, and treatment failure. There was a failure to change patient management following identification of treatment non-adherence at monthly reviews. A better understanding of adherence patterns and how they relate to poor outcomes, coupled with a more timely review of adherence data and improved implementation of differentiated care, may be required. Funding Bill & Melinda Gates Foundation.

The restriction criteria were defined as follows: 1. There are four prefectures (six in Hangzhou, seven in Weizhou, four in Jilin and seven in Ganzhou) in three provinces (13 in Zhejiang, 4 in Jilin and 7 in Jiangxi). The difference in the number of intervention and control group clusters should be at most one in each prefecture (or province).
2. There are 14 clusters with a designated hospital and 10 with a TB dispensary. There should be equal numbers of clusters with a designated hospital in each study group (and therefore equal numbers with a TB dispensary).
3. There are 7 urban clusters and 17 rural clusters. The difference in the number of intervention and control group clusters should be at most one in each of the urban and rural clusters. 4. The difference in the average number of smear-positive TB cases notified by the TB clinic in 2015 in each cluster between intervention and control group clusters is at most 10 cases.
The process for restricted randomization followed three steps: 1. Generate 10,000 random allocations and determine the "restriction factor" for each of the four criteria above. So long as at least 1% of allocations are eligible (giving at least 27,042 eligible allocations) then proceed to step 2. 1.24% of allocations were eligible.
2. If no restrictions or stratifications are applied, the probab7ility that any two clusters are in the same group should be 11/23 = 0.478. To ensure that valid statistical inferences can be drawn, we want to ensure that the probability that any two clusters are in the same group is not too far from 0. 478. For example, if the probability that two clusters are in the same study group is close to 1, then these two clusters may need to be treated as one cluster. Hence, the next step is to check these probabilities for all pairwise combinations (23*22/2 = 253 possibilities). This was done by simulating 5,000 acceptable allocations and then estimating the pairwise probabilities from these 5,000 allocations. The proportion where a pair of clusters were in the same study group varied between 0.288 and 0.745, which suggests that the most restrictive criteria still allows for appropriate statistical inference. Hence, these criteria were finalised for the randomisation.
3. One of the 5,000 can be chosen as the final allocation of the 24 clusters to the two groups. This was chosen using a random number generated by Stata. The random number seed to determine this was selected before any of the above steps were initiated.
All clusters were enrolled before this process began. Randomisation was conducted by James Lewis, the trial statistician at the time.

Measurement of outcomes
The treatment outcomes are based on standard NTP recorded outcomes. Poor end of treatment outcome is defined as treatment failure, died, developed multidrug resistant tuberculosis, lost-to-follow-up, or stopping treatment due to an adverse reaction or refusal of treatment). Treatment failure (as documented on the TB register) is defined as positive sputum at month 5 or later, after treatment start) and on-treatment lost to follow-up (as documented on the TB register) is defined as patient whose treatment was interrupted for 2 consecutive months or more.

Statistical methods
All analyses used a cluster-level analysis containing all clusters. For this, the outcomes were summarised to a mean, proportion, or rate for each cluster. Our primary comparison for binary and rate outcomes were ratios. To compare the study groups, we look logarithms of the cluster summaries and compared the study groups using a t-test. The exponential of the mean difference in log-summaries gave a risk or rate ratio. For the absolute difference between the study groups, the clusters summaries were compared without taking logarithms. Adjustment was done using a two stage approach. First, a model was fit with the outcome as the dependent variable and adjustment covariates as the independent variables ignoring the intervention and clustering. This provided estimated residuals, which were summarised by cluster and analysed as described above.

Multiple imputation
For the primary composite outcome participants were classified as having a favourable outcome if: they had a treatment outcome at the end of treatment of cured or completed treatment and no recurrence defined by either a negative culture at 18 months or at 18 months, culture is missing, or the patient was unable to produce sputum and they have no signs of new active TB meaning (1) no self-reported restart of treatment or (2) no sign of new active TB on x-ray. The composite outcome was missing if a participant meets neither definition of a favourable or unfavourable outcome, particularly an issue if a patient has become lost to follow up after treatment completion, with no sign of a recurrence at their last follow-up visit.
For the primary outcome, multiple imputation was used to prevent bias arising from lost to follow up after completion of treatment. Chained equations were used to imputed poor treatment outcome, recurrence up to 12 months, and recurrence up to 18 months. Recurrence was defined by a positive culture at or before the designated time point, an x-ray indicative of TB, or a self-report of restarting treatment. Non-recurrence required a negative culture at the designated time. Otherwise, recurrence was missing and imputed. Components were simplified to good or poor treatment outcome, recurrence up to 12 months, or recurrence between 12 and 18 months. Imputations used logistic regression models by arm and adjusted for cluster and each of the other components. Data augmentation was used to overcome perfect prediction: this is where a small number of observations are added to the dataset to prevent the imputation model perfectly predicting the outcome. Imputation models included all adjustment covariates, cluster, and the two other components of the model. It was pre-specified that problematic covariates would be removed from the imputation. This resulted in a final set of imputation models that contained only cluster, and the other two component of a poor outcome.
Each imputed dataset was analysed with the cluster-level analysis method described above and the results were combined using Rubin's rules

Complete case analysis
For the primary outcome, as a sensitivity analysis, a complete case analysis was also conducted. For all other outcomes, complete case analysis was the primary analysis. These analyses only included patients with a definable outcome. For the primary composite outcome, classification of no TB recurrence was loosened so that a selfreport of no restart of treatment, or a chest x-ray showing no signs of active TB at 18 months were sufficient for a patient to be classified as having a good composite outcome.

Subgroup analyses
For individual level characteristics, first calculate the risk of a poor outcome in each subgroup and cluster. Calculate the mean of these risks within subgroup to estimate the risk of a poor outcome in control and intervention groups. To test for a difference, calculate the difference between the two subgroups within each cluster. A t-test is performed on these differences to test for effect modification. For cluster level characteristics, a linear regression will be used with study group, subgroup, and an interaction between the two as independent variables. A likelihood ratio test will be used to estimate a p-value for the interaction. The risk of a poor outcome and its confidence interval in each group will be calculated from the relevant coefficients from the regression.

Protocol clarification
There is inconsistency in the description of the frequency of Doctor visits for patients in the control arm in the protocol (Table 2 and appendix to the protocol). In the control arm of the trial the doctor from township health center visit TB patients every 10 days in the intensive phase, and once a month in the continuous phase. This is correctly described in Table 2 from the protocol.

Characteristics of the trial sites and participants
Early in the trial, 19 patients were incorrectly counted as post-enrolment exclusions after enrolment due to misunderstandings of the trial protocols. Additional training was provided to trial sites, and these have been included in the intention to treat population despite missing values for most outcomes.

Per-protocol population
94.7% (1231/1300) and 93.4% (1156/1238) contributed to the per-protocol complete case analysis for the primary outcome, in the control (routine care) and intervention groups, respectively.

Process measures
The rate of medication event reminder monitor (MERM) malfunctions or errors was similar between the groups (0.2 and 0.4 days of treatment affected by errors per person-month in control and intervention groups respectively; adjusted rate ratio=1.75[0.91,3.36]). Patients in the intervention group were more likely to report non-use of the MERM due to travel (intervention arm 1.8% of days not using the intervention vs control arm 0.9% of days not using the MERM). Intervention group patients were 1.30(1.06, 1.60) time more likely than control patients to open the box for a short time that would not be long enough for this to signify taking treatment (<2 seconds). Length of treatment was similar between the groups (mean 6 months control vs 6.1 months intervention, adjusted mean difference=+0.1 [-0.3

,+0.3]).
There was no difference between the groups in withdrawal from use of the MERM (control 105[GM 6%] vs intervention 106[GM 8%]); most of these were due to inability to use fixed dose combination therapy, which was also similar between the groups (control 79[GM 5%] vs intervention 46[GM 4%]).  Number of patients J i a n d e J i a n g g a n L e q i n g L i n 'a n L o n g t a n N i n g d u P a n s h i P i n g y a n g R u i 'a n R u i j i n X i n g g u o Z h a n g g o n g    All sensitivity analysis used complete cases and unless specified used intention to treat populations and were pre-specified in the analysis plan a Percentages shown are arithmetic means of the cluster level risks b Post-hoc: Not pre-specified in analysis plan NTM Non-tuberculous mycobacteria; CI confidence interval.